The Autonomous Vehicles Podcast

In this episode we discuss how self learning AI allows AVs to improve without labeled data, the limitations of requiring training data to improve AV performance, and Autobrains’ go-to-market approach over the next several years.

In this episode we discuss Nexar’s approach to building a “swarm sensor”, and how this modality will enable AVs with higher fidelity training data. We also explore the go-to-market mechanics of bringing a network sensor to the industry.

In this episode we discuss 4D predictive perception, how this approach addresses the existing and future challenges of AV development, and BlueSpace’s go to market strategy over the next few years.

In this episode we discuss teleoperations and why it is important for AVs, the challenges in ensuring safe teleoperations regardless of connectivity, and Imperium Drive’s go to market strategy over the next few years.

In this episode we discuss how AI-powered software can improve the resolution of radar by orders of magnitude, the merits of both software only and full stack approaches to radar development, and how the AV and ADAS markets will continue to develop over the next half decade.

Apple: podcasts.apple.com/us/podcas… Spotify: open.spotify.com/show/3925D8… Stitcher: stitcher.com/show/the-autono… Full Episode List: AutonomousVehiclesPodcast.co…

In this episode we discuss synthetic data and how it is used in training AVs, how AV companies are thinking about buying versus building this data moving forward, and whether or not synthetic data will replace real data in the future.

Apple: podcasts.apple.com/us/podcas… Spotify: open.spotify.com/show/3925D8… Stitcher: stitcher.com/show/the-autono… Full Episode List: AutonomousVehiclesPodcast.co…

In this episode we discuss the data demands of AV companies, how iMerit helps them with complex edge cases, and the biggest challenges that still need to be addressed to further advance autonomous mobility in cities across the US.

In this episode we discuss Opsys’s approach to building a solid state LiDAR, how economies of scale will help drive costs down in the near term, and the company’s go to market approach over the next few years.